Transition to palliative care when transcatheter aortic valve implantation is not an option
Bibliographic record
Abstract
PURPOSE OF REVIEW: Transcatheter aortic valve implantation (TAVI) is the recommended treatment for most patients with symptomatic aortic stenosis at high surgical risk. However, TAVI may be clinically futile for patients who have multiple comorbidities and excessive frailty. This group benefits from transition to palliative care to maximize quality of life, improve symptoms, and ensure continuity of health services. We discuss the clinical determination of utility and futility, explore the current evidence guiding the integration of palliative care in procedure-focused cardiac programs, and outline recommendations for TAVI programs. RECENT FINDINGS: The determination of futility of treatment in elderly patients with aortic stenosis is challenging. There is a paucity of research available to guide best practices when TAVI is not an option. Opportunities exist to build on the evidence gained in the management of end of life and heart failure. TAVI programs and primary care providers can facilitate improved communication and processes of care to provide decision support and transition to palliative care. SUMMARY: The increased availability of transcatheter options for the management of valvular heart disease will increase the assessment of people with life-limiting conditions for whom treatment may not be an option. It is pivotal to bridge cardiac innovation and palliation to optimize patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".